Lessen Database Scanning towards Hiding Sensitive Data based on Binary Search Approach

Alagappa Institute of Skill Development & Computer Centre,Alagappa University, Karaikudi, India.15 -16 February 2017. IT Skills Show & International Conference on Advancements In Computing Resources (SSICACR-2017)

Format: Volume 5, Issue 1, No 17, 2017

Copyright: All Rights Reserved ©2017

Year of Publication: 2017

Author: Dr.K.Kavitha

Reference:IJCS-238

View PDF Format

Abstract

Data mining is an emerging tool for extracting hidden knowledge from huge dataset. Major challenges in data mining are to identify, hidden information with minimum number of database scanning. The main objective is to develop an algorithm to find sensitive patterns such that preserving hidden information. This paper proposes an algorithm that uses pruning step and Binary Search Method for tackling the problems. The problem of association rule mining can be solved by using pruning and binary search method. This paper proposed and algorithm to integrate these two methods for eliminating weak candidate sets and avoiding unnecessary database scanning.

References

[1] Shintre Sonali Sambhaji, Kalyanakar Pravin P. “Mining and Hiding of Sensitive Association Rule by using Improved Apriori Algorithm”, Proceedings of 18th International Conference, Jan 2015 ISBN: 978-93-84209-82-7. [2] Lalit Mohan Goyal, M.M.Sufyan Beg and Tanvir Ahmad, “A Novel Approach for Association Rule Mining”, International Journal of Information Technology, Vol 7, Issn 0973-5658, Jan 2015. [3] Agrawal, R., Imielinski, T., and Swami, A. N. 1993. Mining Assocation rules between sets of Items in large databases. In Proceedings of the 1993 aCM SIGMOD International Conference on Management of Data, 207-216. [4] Agrawal, R., Srikant R, Fast algorithms for mining association rules, Proceedings of the 20th International Conference on Very large databases, New York: IEEE press, PP. 487-499,1994 [5] S. Brin, etc – Dynamic itemset counting and implication rules for market based analysis, Proceedings ACM SIGMOD International Conference of Data, PP. 255-264, 1994. [6] M. J. Zaki, “Scalable algorithms for assoiction mining”, IEEE transaction on knowledge and data engineering, pp.372-390, 2000. [7] J. Han, J. Pei, Y. Yin- Mining frequent patterns without candidate generation. Proceedings of SIGMOD 2000. [8] F.C.Tseng and C.C. Hsu – Generating frequent patterns with the frequent pattern list, Proc. 5th Pacific Asia Conf. on Knowledge Discovery and Data Mining, pp.376-386, April 2001. [9] Nicolas pasquier, yves bastide, rafik taouil, and lotfi lakhal. Discovering frequent closed itemsets for association rules. In proc. ICDT 99, pages 398-416, 1999. [10] M.J.Zaki and C.Hsiano, “Charm: An efficient algorithm for closed itemset mining”, Proc SIAM international conference Data Mining, pp. 457-473, 2002. [11] J. Pei, J. Han and R. Mao, “Closet: An efficient algorithm for mining frequent closed itemsets”, ACM SIGMOD workshop research issue in Data mining and knowledge discovery, pp. 21-30, 2000. [12] J.Wang, J.Han and J.Pei “Closet: Searching for the best strategies for mining frequent closed itemsets”, proc. Intl conf. knowledge discovery and data mining, pp. 236-245, 2003. [13] G. Grahne, and J.Zhu “Effciently using prefix trees in mining frequent itemsets”, IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2003.


Keywords

Association Rule, Apriori Algorithm, Minimum Association Rule Mining Support, Minimum Candidate Threshold.

This work is licensed under a Creative Commons Attribution 3.0 Unported License.   

TOP
Facebook IconYouTube IconTwitter IconVisit Our Blog